Overview

Dataset statistics

Number of variables11
Number of observations18562
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.5 MiB
Average record size in memory82.0 B

Variable types

Numeric9
Boolean2

Alerts

MedInc is highly overall correlated with AveRooms and 1 other fieldsHigh correlation
AveRooms is highly overall correlated with MedIncHigh correlation
Latitude is highly overall correlated with Longitude and 2 other fieldsHigh correlation
Longitude is highly overall correlated with Latitude and 2 other fieldsHigh correlation
MedHouseVal is highly overall correlated with MedIncHigh correlation
west_of_lon120 is highly overall correlated with Latitude and 2 other fieldsHigh correlation
north_of_lat36 is highly overall correlated with Latitude and 2 other fieldsHigh correlation

Reproduction

Analysis started2023-05-26 17:38:25.158765
Analysis finished2023-05-26 17:38:35.894845
Duration10.74 seconds
Software versionydata-profiling vv4.1.2
Download configurationconfig.json

Variables

MedInc
Real number (ℝ)

Distinct11684
Distinct (%)62.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.6745204
Minimum0.4999
Maximum9.9055
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size290.0 KiB
2023-05-26T19:38:35.997537image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0.4999
5-th percentile1.60003
Q12.539275
median3.4643
Q34.5893
95-th percentile6.526765
Maximum9.9055
Range9.4056
Interquartile range (IQR)2.050025

Descriptive statistics

Standard deviation1.5241343
Coefficient of variation (CV)0.41478455
Kurtosis0.35319227
Mean3.6745204
Median Absolute Deviation (MAD)1.006
Skewness0.72499648
Sum68206.447
Variance2.3229853
MonotonicityNot monotonic
2023-05-26T19:38:36.184846image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2.875 42
 
0.2%
3.125 42
 
0.2%
3.875 36
 
0.2%
4.125 36
 
0.2%
3.625 35
 
0.2%
3 35
 
0.2%
2.625 35
 
0.2%
4.375 34
 
0.2%
4 34
 
0.2%
3.375 32
 
0.2%
Other values (11674) 18201
98.1%
ValueCountFrequency (%)
0.4999 2
< 0.1%
0.536 4
< 0.1%
0.5495 1
 
< 0.1%
0.6433 1
 
< 0.1%
0.6775 1
 
< 0.1%
0.6825 1
 
< 0.1%
0.6831 1
 
< 0.1%
0.6991 1
 
< 0.1%
0.7007 1
 
< 0.1%
0.7054 1
 
< 0.1%
ValueCountFrequency (%)
9.9055 1
< 0.1%
9.8346 1
< 0.1%
9.8074 1
< 0.1%
9.7037 1
< 0.1%
9.6986 1
< 0.1%
9.6062 1
< 0.1%
9.6023 1
< 0.1%
9.5908 1
< 0.1%
9.5862 1
< 0.1%
9.5823 1
< 0.1%

HouseAge
Real number (ℝ)

Distinct52
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean28.889613
Minimum1
Maximum52
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size290.0 KiB
2023-05-26T19:38:36.358868image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile9
Q119
median29
Q337
95-th percentile52
Maximum52
Range51
Interquartile range (IQR)18

Descriptive statistics

Standard deviation12.348483
Coefficient of variation (CV)0.42743678
Kurtosis-0.78539152
Mean28.889613
Median Absolute Deviation (MAD)9
Skewness0.040759735
Sum536249
Variance152.48504
MonotonicityNot monotonic
2023-05-26T19:38:36.528692image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
52 1059
 
5.7%
36 803
 
4.3%
35 774
 
4.2%
16 692
 
3.7%
34 633
 
3.4%
17 612
 
3.3%
26 577
 
3.1%
33 573
 
3.1%
32 519
 
2.8%
25 511
 
2.8%
Other values (42) 11809
63.6%
ValueCountFrequency (%)
1 3
 
< 0.1%
2 33
 
0.2%
3 45
 
0.2%
4 142
0.8%
5 199
1.1%
6 140
0.8%
7 138
0.7%
8 167
0.9%
9 178
1.0%
10 227
1.2%
ValueCountFrequency (%)
52 1059
5.7%
51 43
 
0.2%
50 119
 
0.6%
49 125
 
0.7%
48 163
 
0.9%
47 190
 
1.0%
46 228
 
1.2%
45 274
 
1.5%
44 331
 
1.8%
43 334
 
1.8%

AveRooms
Real number (ℝ)

Distinct17476
Distinct (%)94.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.1644141
Minimum0.84615385
Maximum9.8307692
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size290.0 KiB
2023-05-26T19:38:36.705450image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0.84615385
5-th percentile3.4317002
Q14.3965104
median5.1412284
Q35.8840728
95-th percentile7.0609231
Maximum9.8307692
Range8.9846154
Interquartile range (IQR)1.4875624

Descriptive statistics

Standard deviation1.1102088
Coefficient of variation (CV)0.21497284
Kurtosis0.15960843
Mean5.1644141
Median Absolute Deviation (MAD)0.74402219
Skewness0.1533985
Sum95861.854
Variance1.2325635
MonotonicityNot monotonic
2023-05-26T19:38:36.867868image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5 27
 
0.1%
4.5 20
 
0.1%
6 18
 
0.1%
4 18
 
0.1%
5.333333333 12
 
0.1%
5.5 11
 
0.1%
4.666666667 8
 
< 0.1%
5.666666667 7
 
< 0.1%
6.090909091 7
 
< 0.1%
7 6
 
< 0.1%
Other values (17466) 18428
99.3%
ValueCountFrequency (%)
0.8461538462 1
< 0.1%
1 1
< 0.1%
1.130434783 1
< 0.1%
1.260869565 1
< 0.1%
1.378486056 1
< 0.1%
1.411290323 1
< 0.1%
1.465753425 1
< 0.1%
1.550408719 1
< 0.1%
1.553030303 1
< 0.1%
1.598130841 1
< 0.1%
ValueCountFrequency (%)
9.830769231 1
< 0.1%
9.691891892 1
< 0.1%
9.505154639 1
< 0.1%
9.466666667 1
< 0.1%
9.431884058 1
< 0.1%
9.286821705 1
< 0.1%
9.263598326 1
< 0.1%
9.232253086 1
< 0.1%
9.142857143 1
< 0.1%
9 1
< 0.1%

AveBedrms
Real number (ℝ)

Distinct12837
Distinct (%)69.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.0537599
Minimum0.8
Maximum1.4
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size290.0 KiB
2023-05-26T19:38:37.036426image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0.8
5-th percentile0.94002864
Q11.0041852
median1.0459409
Q31.0931911
95-th percentile1.1965374
Maximum1.4
Range0.6
Interquartile range (IQR)0.089005917

Descriptive statistics

Standard deviation0.079690275
Coefficient of variation (CV)0.075624698
Kurtosis1.9641624
Mean1.0537599
Median Absolute Deviation (MAD)0.044139102
Skewness0.82841005
Sum19559.892
Variance0.00635054
MonotonicityNot monotonic
2023-05-26T19:38:37.203110image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 265
 
1.4%
1.058823529 25
 
0.1%
1.125 24
 
0.1%
1.052631579 23
 
0.1%
1.083333333 22
 
0.1%
1.1 22
 
0.1%
1.05 21
 
0.1%
1.090909091 20
 
0.1%
1.055555556 18
 
0.1%
1.076923077 17
 
0.1%
Other values (12827) 18105
97.5%
ValueCountFrequency (%)
0.8 7
< 0.1%
0.8058823529 1
 
< 0.1%
0.8064516129 1
 
< 0.1%
0.8085106383 1
 
< 0.1%
0.8098159509 1
 
< 0.1%
0.8125 1
 
< 0.1%
0.8130841121 1
 
< 0.1%
0.8134920635 1
 
< 0.1%
0.813559322 1
 
< 0.1%
0.8155339806 1
 
< 0.1%
ValueCountFrequency (%)
1.4 4
< 0.1%
1.399558499 1
 
< 0.1%
1.397959184 1
 
< 0.1%
1.397590361 1
 
< 0.1%
1.396959459 1
 
< 0.1%
1.396825397 1
 
< 0.1%
1.396666667 1
 
< 0.1%
1.394736842 1
 
< 0.1%
1.394230769 1
 
< 0.1%
1.393767705 1
 
< 0.1%

Population
Real number (ℝ)

Distinct3519
Distinct (%)19.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1377.4422
Minimum5
Maximum4992
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size290.0 KiB
2023-05-26T19:38:37.392006image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum5
5-th percentile403
Q1816
median1188
Q31726
95-th percentile3058.9
Maximum4992
Range4987
Interquartile range (IQR)910

Descriptive statistics

Standard deviation823.40117
Coefficient of variation (CV)0.59777548
Kurtosis2.4498016
Mean1377.4422
Median Absolute Deviation (MAD)429
Skewness1.4058855
Sum25568082
Variance677989.48
MonotonicityNot monotonic
2023-05-26T19:38:37.584094image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1227 24
 
0.1%
1052 24
 
0.1%
891 24
 
0.1%
782 22
 
0.1%
1098 21
 
0.1%
781 21
 
0.1%
872 21
 
0.1%
850 21
 
0.1%
1005 21
 
0.1%
1047 20
 
0.1%
Other values (3509) 18343
98.8%
ValueCountFrequency (%)
5 1
 
< 0.1%
6 1
 
< 0.1%
8 3
< 0.1%
9 1
 
< 0.1%
11 1
 
< 0.1%
13 2
< 0.1%
14 1
 
< 0.1%
15 2
< 0.1%
19 1
 
< 0.1%
22 1
 
< 0.1%
ValueCountFrequency (%)
4992 1
< 0.1%
4985 1
< 0.1%
4983 1
< 0.1%
4976 1
< 0.1%
4970 1
< 0.1%
4956 1
< 0.1%
4952 1
< 0.1%
4951 1
< 0.1%
4945 1
< 0.1%
4944 1
< 0.1%

AveOccup
Real number (ℝ)

Distinct17118
Distinct (%)92.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.9314308
Minimum0.97058824
Maximum6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size290.0 KiB
2023-05-26T19:38:37.754844image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0.97058824
5-th percentile1.9019506
Q12.4528869
median2.8425133
Q33.3034155
95-th percentile4.2999635
Maximum6
Range5.0294118
Interquartile range (IQR)0.85052856

Descriptive statistics

Standard deviation0.71674177
Coefficient of variation (CV)0.24450236
Kurtosis1.021369
Mean2.9314308
Median Absolute Deviation (MAD)0.41973565
Skewness0.7840321
Sum54413.219
Variance0.51371877
MonotonicityNot monotonic
2023-05-26T19:38:38.030118image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3 28
 
0.2%
2 14
 
0.1%
2.666666667 12
 
0.1%
2.5 12
 
0.1%
3.2 11
 
0.1%
2.555555556 9
 
< 0.1%
2.8 9
 
< 0.1%
2.6 9
 
< 0.1%
2.333333333 8
 
< 0.1%
4 8
 
< 0.1%
Other values (17108) 18442
99.4%
ValueCountFrequency (%)
0.9705882353 1
< 0.1%
1.066176471 1
< 0.1%
1.089267803 1
< 0.1%
1.161290323 1
< 0.1%
1.169329073 1
< 0.1%
1.215873016 1
< 0.1%
1.239616613 1
< 0.1%
1.244556114 1
< 0.1%
1.24516129 1
< 0.1%
1.263565891 1
< 0.1%
ValueCountFrequency (%)
6 1
< 0.1%
5.995680346 1
< 0.1%
5.96941896 1
< 0.1%
5.961077844 1
< 0.1%
5.950248756 1
< 0.1%
5.941176471 1
< 0.1%
5.940340909 1
< 0.1%
5.933554817 1
< 0.1%
5.927066451 1
< 0.1%
5.923076923 1
< 0.1%

Latitude
Real number (ℝ)

Distinct839
Distinct (%)4.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean35.630331
Minimum32.54
Maximum41.95
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size290.0 KiB
2023-05-26T19:38:38.228369image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum32.54
5-th percentile32.81
Q133.93
median34.26
Q337.72
95-th percentile38.9
Maximum41.95
Range9.41
Interquartile range (IQR)3.79

Descriptive statistics

Standard deviation2.1308925
Coefficient of variation (CV)0.059805577
Kurtosis-1.1255001
Mean35.630331
Median Absolute Deviation (MAD)1.28
Skewness0.45389167
Sum661370.21
Variance4.5407029
MonotonicityNot monotonic
2023-05-26T19:38:38.430478image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
34.08 215
 
1.2%
34.05 203
 
1.1%
34.09 200
 
1.1%
34.02 190
 
1.0%
34.07 189
 
1.0%
34.06 188
 
1.0%
34.04 188
 
1.0%
34.1 184
 
1.0%
34.03 179
 
1.0%
33.93 174
 
0.9%
Other values (829) 16652
89.7%
ValueCountFrequency (%)
32.54 1
 
< 0.1%
32.55 3
 
< 0.1%
32.56 9
 
< 0.1%
32.57 18
0.1%
32.58 26
0.1%
32.59 11
0.1%
32.6 8
 
< 0.1%
32.61 14
0.1%
32.62 13
0.1%
32.63 16
0.1%
ValueCountFrequency (%)
41.95 2
< 0.1%
41.92 1
 
< 0.1%
41.88 1
 
< 0.1%
41.86 3
< 0.1%
41.84 1
 
< 0.1%
41.81 1
 
< 0.1%
41.8 3
< 0.1%
41.78 3
< 0.1%
41.77 1
 
< 0.1%
41.76 2
< 0.1%

Longitude
Real number (ℝ)

Distinct789
Distinct (%)4.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-119.58285
Minimum-124.35
Maximum-114.55
Zeros0
Zeros (%)0.0%
Negative18562
Negative (%)100.0%
Memory size290.0 KiB
2023-05-26T19:38:38.628266image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum-124.35
5-th percentile-122.46
Q1-121.79
median-118.495
Q3-118.01
95-th percentile-117.09
Maximum-114.55
Range9.8
Interquartile range (IQR)3.78

Descriptive statistics

Standard deviation1.9927056
Coefficient of variation (CV)-0.016663808
Kurtosis-1.3509814
Mean-119.58285
Median Absolute Deviation (MAD)1.285
Skewness-0.30175907
Sum-2219696.9
Variance3.9708758
MonotonicityNot monotonic
2023-05-26T19:38:38.811173image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-118.3 155
 
0.8%
-118.31 155
 
0.8%
-118.29 142
 
0.8%
-118.27 142
 
0.8%
-118.28 135
 
0.7%
-118.19 129
 
0.7%
-118.35 129
 
0.7%
-118.36 128
 
0.7%
-118.32 128
 
0.7%
-118.25 125
 
0.7%
Other values (779) 17194
92.6%
ValueCountFrequency (%)
-124.35 1
 
< 0.1%
-124.3 2
 
< 0.1%
-124.27 1
 
< 0.1%
-124.26 1
 
< 0.1%
-124.23 3
 
< 0.1%
-124.22 1
 
< 0.1%
-124.21 3
 
< 0.1%
-124.19 4
 
< 0.1%
-124.18 6
< 0.1%
-124.17 12
0.1%
ValueCountFrequency (%)
-114.55 1
 
< 0.1%
-114.57 2
< 0.1%
-114.58 2
< 0.1%
-114.59 2
< 0.1%
-114.6 3
< 0.1%
-114.61 3
< 0.1%
-114.62 1
 
< 0.1%
-114.63 1
 
< 0.1%
-114.64 1
 
< 0.1%
-114.65 2
< 0.1%

MedHouseVal
Real number (ℝ)

Distinct3799
Distinct (%)20.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.9372673
Minimum0.14999
Maximum5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size290.0 KiB
2023-05-26T19:38:38.995396image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0.14999
5-th percentile0.66
Q11.18
median1.754
Q32.5
95-th percentile3.84795
Maximum5
Range4.85001
Interquartile range (IQR)1.32

Descriptive statistics

Standard deviation0.97362999
Coefficient of variation (CV)0.50257908
Kurtosis0.1196008
Mean1.9372673
Median Absolute Deviation (MAD)0.64
Skewness0.78594188
Sum35959.555
Variance0.94795536
MonotonicityNot monotonic
2023-05-26T19:38:39.235874image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.375 103
 
0.6%
1.625 99
 
0.5%
1.125 85
 
0.5%
1.875 85
 
0.5%
2.25 83
 
0.4%
3.5 72
 
0.4%
0.875 65
 
0.4%
1.5 60
 
0.3%
2.75 59
 
0.3%
1.75 57
 
0.3%
Other values (3789) 17794
95.9%
ValueCountFrequency (%)
0.14999 1
 
< 0.1%
0.175 1
 
< 0.1%
0.225 2
< 0.1%
0.25 1
 
< 0.1%
0.266 1
 
< 0.1%
0.269 1
 
< 0.1%
0.3 1
 
< 0.1%
0.325 3
< 0.1%
0.332 1
 
< 0.1%
0.344 1
 
< 0.1%
ValueCountFrequency (%)
5 26
0.1%
4.991 1
 
< 0.1%
4.99 1
 
< 0.1%
4.988 1
 
< 0.1%
4.987 1
 
< 0.1%
4.984 1
 
< 0.1%
4.976 1
 
< 0.1%
4.974 1
 
< 0.1%
4.964 2
 
< 0.1%
4.96 1
 
< 0.1%

west_of_lon120
Boolean

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size163.1 KiB
False
11150 
True
7412 
ValueCountFrequency (%)
False 11150
60.1%
True 7412
39.9%
2023-05-26T19:38:39.437844image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

north_of_lat36
Boolean

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size163.1 KiB
False
10589 
True
7973 
ValueCountFrequency (%)
False 10589
57.0%
True 7973
43.0%
2023-05-26T19:38:39.633601image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Interactions

2023-05-26T19:38:33.885305image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-26T19:38:25.683878image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-26T19:38:26.538768image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-26T19:38:27.534665image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-26T19:38:28.459673image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-26T19:38:29.429295image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-26T19:38:30.663005image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-26T19:38:31.725247image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-26T19:38:32.708627image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-26T19:38:34.035636image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-26T19:38:25.779543image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-26T19:38:26.637868image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-26T19:38:27.631146image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-26T19:38:28.564752image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-26T19:38:29.532244image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-26T19:38:30.766707image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-26T19:38:31.831175image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-26T19:38:32.810494image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-26T19:38:34.190561image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-26T19:38:25.884634image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-26T19:38:26.732603image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-26T19:38:27.737427image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-26T19:38:28.674178image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-26T19:38:29.640074image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-26T19:38:30.871570image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-26T19:38:31.952921image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-26T19:38:32.929136image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-26T19:38:34.509855image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-26T19:38:25.972757image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-26T19:38:26.921785image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-26T19:38:27.831523image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-26T19:38:28.777182image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-26T19:38:29.742614image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-26T19:38:30.981424image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-26T19:38:32.083154image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-26T19:38:33.039008image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-26T19:38:34.680013image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-26T19:38:26.072466image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-26T19:38:27.020424image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-26T19:38:27.938723image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-26T19:38:28.896049image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-26T19:38:30.040008image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-26T19:38:31.108812image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-26T19:38:32.191268image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-26T19:38:33.175783image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-26T19:38:34.818573image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-26T19:38:26.161358image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-26T19:38:27.122183image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-26T19:38:28.033923image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-26T19:38:28.997080image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-26T19:38:30.195553image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-26T19:38:31.226529image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-26T19:38:32.305006image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-26T19:38:33.323738image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-26T19:38:34.972756image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-26T19:38:26.269536image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-26T19:38:27.240128image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-26T19:38:28.148846image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-26T19:38:29.109094image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-26T19:38:30.317201image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-26T19:38:31.346172image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-26T19:38:32.406150image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-26T19:38:33.479382image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-26T19:38:35.116928image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-26T19:38:26.359579image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-26T19:38:27.338829image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-26T19:38:28.241530image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-26T19:38:29.209486image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-26T19:38:30.434160image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-26T19:38:31.466832image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-26T19:38:32.507892image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-26T19:38:33.612129image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-26T19:38:35.271523image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-26T19:38:26.452204image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-26T19:38:27.435669image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-26T19:38:28.358468image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-26T19:38:29.326557image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-26T19:38:30.545856image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-26T19:38:31.603337image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-26T19:38:32.607286image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-26T19:38:33.742272image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Correlations

2023-05-26T19:38:39.975929image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
MedIncHouseAgeAveRoomsAveBedrmsPopulationAveOccupLatitudeLongitudeMedHouseValwest_of_lon120north_of_lat36
MedInc1.000-0.1830.671-0.2690.007-0.041-0.076-0.0030.6520.0290.034
HouseAge-0.1831.000-0.228-0.087-0.293-0.0160.031-0.1380.0490.1640.160
AveRooms0.671-0.2281.0000.004-0.0750.0440.136-0.0560.2490.1560.171
AveBedrms-0.269-0.0870.0041.0000.072-0.1020.041-0.008-0.1240.0370.033
Population0.007-0.293-0.0750.0721.0000.234-0.1170.1220.0140.1150.124
AveOccup-0.041-0.0160.044-0.1020.2341.000-0.1690.196-0.2560.2230.201
Latitude-0.0760.0310.1360.041-0.117-0.1691.000-0.885-0.1580.9360.988
Longitude-0.003-0.138-0.056-0.0080.1220.196-0.8851.000-0.0660.9810.936
MedHouseVal0.6520.0490.249-0.1240.014-0.256-0.158-0.0661.0000.0960.216
west_of_lon1200.0290.1640.1560.0370.1150.2230.9360.9810.0961.0000.879
north_of_lat360.0340.1600.1710.0330.1240.2010.9880.9360.2160.8791.000

Missing values

2023-05-26T19:38:35.476643image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
A simple visualization of nullity by column.
2023-05-26T19:38:35.751950image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

MedIncHouseAgeAveRoomsAveBedrmsPopulationAveOccupLatitudeLongitudeMedHouseValwest_of_lon120north_of_lat36
08.325241.06.9841271.023810322.02.55555637.88-122.234.526TrueTrue
18.301421.06.2381370.9718802401.02.10984237.86-122.223.585TrueTrue
27.257452.08.2881361.073446496.02.80226037.85-122.243.521TrueTrue
35.643152.05.8173521.073059558.02.54794537.85-122.253.413TrueTrue
43.846252.06.2818531.081081565.02.18146737.85-122.253.422TrueTrue
54.036852.04.7616581.103627413.02.13989637.85-122.252.697TrueTrue
63.659152.04.9319070.9513621094.02.12840537.84-122.252.992TrueTrue
73.120052.04.7975271.0618241157.01.78825337.84-122.252.414TrueTrue
82.080442.04.2941181.1176471206.02.02689137.84-122.262.267TrueTrue
93.691252.04.9705880.9901961551.02.17226937.84-122.252.611TrueTrue
MedIncHouseAgeAveRoomsAveBedrmsPopulationAveOccupLatitudeLongitudeMedHouseValwest_of_lon120north_of_lat36
206303.567311.05.9325841.1348311257.02.82471939.29-121.321.120TrueTrue
206313.517915.06.1458331.1412041200.02.77777839.33-121.401.072TrueTrue
206323.125015.06.0233771.0805191047.02.71948139.26-121.451.156TrueTrue
206332.549527.05.4450261.0785341082.02.83246139.19-121.530.983TrueTrue
206343.712528.06.7790701.1482561041.03.02616339.27-121.561.168TrueTrue
206351.560325.05.0454551.133333845.02.56060639.48-121.090.781TrueTrue
206362.556818.06.1140351.315789356.03.12280739.49-121.210.771TrueTrue
206371.700017.05.2055431.1200921007.02.32563539.43-121.220.923TrueTrue
206381.867218.05.3295131.171920741.02.12320939.43-121.320.847TrueTrue
206392.388616.05.2547171.1622641387.02.61698139.37-121.240.894TrueTrue